With the significant expansion of the breadth and depth of financial services for the real economy, the real sector's "be diverted out of the real economy" trend has become a structural contradiction, and the motivation and appropriateness behind the enterprise's financialization have become an urgent issue to be resolved. This paper starts from the enterprise's response to risk and uncertainty, as well as the financialization drive mechanism to pursue shareholder value, and constructs the optimization problem of the investment portfolio intending to minimize enterprise risk and maximize shareholder value, and then obtains the optimal allocation of financial assets under different objectives reflecting the relationship between enterprise risk, shareholder value and the non-linear relationship between the levels of enterprise financialization. On this basis, based on the fact that the enterprise's financialization driving mechanism reflects the characteristics of the financialization motivation, we start from the enterprise's asset allocation structure. Combined with the optimal allocation of financial assets under specific objectives, we propose the criteria for determining the enterprise's financialization motivation and the appropriate scope of the enterprise's financialization. Then, we selected the representative listed non-financial enterprises in China as a sample for empirical analysis:As for the identification of the motivation of financialization, this paper found that although the non-financial enterprise sector is still dominated by the "reservoir" motivation, the whole, the "investment substitution" motivation is more significant and cannot be ignored. There is heterogeneity among different industries and enterprises with different equity natures, among which the motivation of "investment substitution" in the wholesale and retail industry and real estate industry is significant, and the motivation of "investment substitution" in non-state-owned enterprises is significantly stronger than that in state-owned enterprises. Concerning the appropriateness of enterprise financialization, we find that the overall financialization appropriateness of the non-financial enterprise sector is low. Especially in the information technology industry and the power, gas, and water production and supply industry, the appropriateness of financialization is significantly lower than the average level, and the level of industry financialization is insufficient. In contrast, the wholesale, retail, and real estate industries have excessive financialization. The moderation of non-state-owned enterprises' financialization is significantly higher than that of state-owned enterprises, and there is also the phenomenon of excessive financialization. The conclusion of this paper provides guiding suggestions for financial investment decision-making and regulatory identification of enterprises.

Customization, as an important customer value strategy for corporate development, is of great significance to the improvement of customer satisfaction and the increase of firm revenue. As digital technologies such as artificial intelligence, big data, and cloud computing develop quickly, firms' customization practices and related academic research meet great opportunities and challenges. Based on this background, through systematic and comprehensive analysis of Chinese and English literatures on customization, this paper clarifies and analyses the definition and patterns of customization, the key influencing factors of customization capabilities, the performance outcomes and the corresponding theoretical mechanisms of customization strategy, and the boundary conditions of the effectiveness of customization strategy. On this basis, this paper then summaries an integrated research framework for customization literature and proposes future research directions for customization from the aspects of patterns, performance outcomes and their mechanism, influencing factors, and the new forms of customer customization in the digital environment, to provide a reference for the development of more in-depth and systematic customization theory, and to bring inspiration for understanding and guiding firms' customization practice.

Since OpenAI launched its artificial intelligence generative content (AIGC) product — ChatGPT on November 2022, the whole world has turned upside down. The AIGC mainly come two main streams: Large language models (LLMs) and diffusion models. New application and research publish daily in an accelerated way. In this paper, we first raise a serious question over LLMs: Does intelligent ability generated from LLMs really owns general artificial intelligence (AGI, artificial general intelligence) ability like ordinary people's intelligence ability doing things?In this paper, I first make an important hypothesis: As a closed system, through a large language model (LLM) has been designed to represent, store human's huge knowledge and intelligence's ability and behavior, equipped with the highest value standard that the model must align to human value, but the LLM model doesn't demonstrate its AGI ability. However, as an opened system, once we input some formatted text with implicit human's knowledge and intelligence, then we suddenly find that output of the LLM model show natures of certain human's intelligence and behavior. The formatted input text is called a prompt. The higher intelligent prompt is, the better intelligent output of the model will be. In other words, the LLM models own some kind of AGI ability conditioned on prompt.Economics research and other social science research such as politics, history, and linguistics include the most complex social forms and the deepest human minds, so in this paper, we try to explore whether AGI-like general AI for large language models LLMs is a fact or an illusion by summarizing the latest research results of other researchers? For this model's AGI-like general AI capabilities, we summarize the latest research results of these research scholars, including issues such as IQ levels of large language models, AIGC industrial economics, computational social science research under AIGC, business decision making, and virtual AIGC economist paradigm research in economics and other social sciences.

Option pricing with discrete dividend payments is still a challenge. This paper proposes a novel model by taking the dividends into consideration, and establishes the option price theorem for obtaining the option price. Theoretical analysis shows that the proposed new model can fully take the impact of dividend payments on option price such as the dividend paying time, amount and number, and hence it can produce an accurate price for option. We also conduct a theoretical comparison of the pricing between the newly-proposed model and classic/benchmark, with which the relation and pricing differences between the new model and these models are deeply detected. The numerical results also show that the proposed model can produce highly accurate prices for options and has strong pricing robustness. Based on this, our model can be an excellent alternative of pricing European options written on the underlying asset paying discrete dividends.

Large models, exemplified by ChatGPT, represent a significant breakthrough in general generative artificial intelligence technology. Their far-reaching implications extend into diverse facets of human production, lifestyle, and cognitive processes, prompting a transformative paradigm shift in the realm of economic research. Originating from the convergence of big data and artificial intelligence, these large models introduce a novel approach to systemic analysis, particularly adept at scrutinizing intricate human economic and social systems. We first discuss the fundamental characteristics and development paradigms of ChatGPT and large models, focusing on how these models effectively tackle the methodological challenges posed by the "curse of dimensionality". We then delve into how ChatGPT and large models will influence the paradigm of economic research. This includes a shift from the assumption of the rational economic man to an AI-driven "human-machine hybrid" economic agent, from the isolated economic individual to the socio-economic individual whose behaviors are measurable, from the separation of macroeconomics and microeconomics to their integration, from the separation of qualitative and quantitative analysis to their unification, and from the long-dominant "small-model" paradigm to a "large-model" paradigm in economic research. We also cover the increasing significance of computer algorithms as a prominent research paradigm and method in economics. Finally, we point out the limitations inherent in artificial intelligence technologies, including large models, when employed as a research method in economics and the broader social sciences.

In recent years, with the significant increase of various security risk events, the supply chain of a large number of enterprises has been impacted or even interrupted. Supply chain resilience has become the common focus of academy, industry and government. From the perspective of single and multiple configurations, this paper constructs research models for the influencing factors of supply chain resilience. Based on 622 valid samples of enterprises, this study applies structural equation model (SEM) and fuzzy set qualitative comparative analysis (fsQCA) for empirical analysis. The research shows that: 1) Flexibility, agility, reconfiguration, visibility and supply chain cooperation can have positive impacts on supply chain resilience. 2) High flexibility, high agility, high reconfiguration, high visibility and strong cooperative relationship cannot be the necessary conditions for high supply chain resilience alone. 3) There are three driving configurations for high supply chain resilience, namely, high agility configuration consisting of agility * reconfiguration * supply chain cooperation, high visibility configuration consisting of ~flexibility * reconfiguration * visibility * supply chain cooperation, and agile visual dual configuration consisting of ~flexibility * agility * visibility * supply chain cooperation and flexibility * agility * visibility * reconfiguration. The high supply chain resilience is the synergy of multiple factors, and the effective combination of various factors promotes the supply chain resilience in the way of "different paths lead to the same goal". This research systematically discusses the influencing factors of supply chain resilience, which can provide theoretical guidance and managerial insight for enterprises to improve supply chain resilience.

The accurate forecasting of stock market volatility is of great theoretical and practical significance for investors to predict stock market trend, optimize asset allocation and avoid risks, and for regulators to warn risks and stabilize market order. In this paper, on the basis of HAR model based on high-frequency trading data, Lasso and random forest method in machine learning are combined to conduct model feature selection, and the nonlinear characteristics among variables are depicted by neural network method, so as to construct several new realized volatility models based on machine learning. Then, the performance of various models in forecasting the realized volatility of Shanghai stock index is evaluated and compared. The empirical results show that, the introduction of the jump component can improve the out-of-sample forecasting accuracy of realized volatility in the stock market. The HAR extended models based on Lasso and random forest for feature selection have significantly better out-of-sample prediction performance than the traditional HAR models and GARCH models. Using the neural network method to describe the nonlinear characteristics of volatility can further improve the out-of-sample prediction accuracy of the model. The Lasso-NN-J model has the best in-sample and out-sample prediction performance among all the investigated forecasting models, and the prediction performance of the model is quite robust under the simulation tests of different rolling window widths, different high-frequency data of individual stocks and random sampling.

It is with great pleasure and admiration that we celebrate the 60th birthday of Professor Lihua Xie, a distinguished researcher and visionary leader in the field of robust control and estimation. Prof. Xie’s remarkable journey, marked by outstanding achievements and groundbreaking contributions, has left an indelible mark on the world of engineering and academia.
Prof. Xie’s academic odyssey began at Nanjing University of Science and Technology, where he earned his bachelor’s and master’s degrees in 1983 and 1986, respectively. His pursuit of knowledge led him to the University of Newcastle, Australia, where he obtained his PhD in 1992. Since 1992, he has been a cornerstone of Nanyang Technological University (NTU), Singapore, currently serving as a distinguished professor in the School of Electrical and Electronic Engineering and as the Director of the Centre for Advanced Robotics Technology Innovation (CARTIN), NTU.
One of Prof. Xie’s pivotal contributions lies in the realm of robust control and estimation. His early work in the early 1990s addressed robust solutions for systems with parametric uncertainties, providing a profound understanding of how uncertainty influences control system performance. His pioneering research not only illuminated the impact of uncertainty but also offered effective strategies, particularly for parametric uncertainty, ensuring the robustness of control systems. Prof. Xie was among the first to develop robust estimation techniques for systems grappling with parametric uncertainties, influencing researchers globally since the 1990s.
In the past two decades, Prof. Xie, alongside his co-author, established a groundbreaking equivalence between quantized feedback and robust control. This breakthrough extended the applicability of existing robust control theory to the analysis and design of control systems operating under quantized feedback. His work also unraveled the intricate interplay among data rate, network topology, and agent dynamics in multi-agent consensus - a fundamental challenge in cooperative control. Prof. Xie’s research provided answers to crucial questions, such as determining the minimal data rate and network topology for multi-agent consensus, along with corresponding coding and decoding schemes.
The spectrum of Prof. Xie’s impact extends to compressive sensing, where he and his student established a phase transition relationship between sparsity and recoverability for complex signals. Their continuous compressive sensing algorithms and Vandermonde decomposition theory for multi-level Toeplitz matrices have found applications in array signal processing, marking another significant milestone in his illustrious career.
Beyond theoretical endeavors, Prof. Xie’s practical innovations have revolutionized localization and unmanned systems. His research group’s developments include a WiFi-based indoor positioning system, multi-modality sensor fusion technology, and a fully integrated navigation solution for UAVs. These innovations have found applications in diverse fields, from structure inspection and delivery using UAVs to a low-cost universal navigation system for AGVs in logistics and manufacturing.
In the realm of research and development leadership, Prof. Xie’s impact is equally profound. He is the founding Director of the Delta-NTU Corporate Laboratory for Cyber-physical Systems, which focuses on the development of smart manufacturing and smart learning technologies for industry. Additionally, Prof. Xie established the Centre for Advanced Robotics Technology Innovation, where he currently serves as the Director. The center’s mission is to pioneer advanced sensing and perception technologies, as well as collaborative robotics technologies, with applications in logistics, manufacturing, and elderly care.
As an accomplished researcher, Prof. Xie has demonstrated unparalleled dedication to serving the research community. His extensive editorial roles, including a founding Editor-inChief for Unmanned Systems and Associate Editor for Sciences China - Information Science, showcase his commitment to advancing scientific knowledge. He has played pivotal roles in various editorial boards, such as IET Book Series in Control and esteemed journals like IEEE Transactions on Automatic Control and Automatica.
Prof. Xie’s impact extends beyond editorial responsibilities; he has been a distinguished IEEE Distinguished Lecturer, a Board of Governors member for the IEEE Control System Society, and Vice President since January 2024. His leadership roles also include serving as General Chair of significant conferences, including the 62nd IEEE Conference on Decision and Control in December 2023.
His professional achievements, recognized by peers worldwide, include fellowships in the Academy of Engineering Singapore, the Institute of Electrical and Electronics Engineers (IEEE), International Federation of Automatic Control (IFAC), and the Chinese Automation Association (CAA).
In celebration of Prof. Xie’s 60th birthday, we invited 17 papers from friends and colleagues for this special issue. As editors, we extend our deepest gratitude to all the authors for their invaluable contributions. Special thanks to the Journal of Systems Science & Complexity editorial office, including Prof. Xiao-Shan Gao (Editor-in-Chief), Prof. Yanlong Zhao (Managing Editor), and Ms. Guoyun Wu (Editorial Director), for their steadfast support from the conception to the publication of this special issue.
On this momentous occasion, we express our profound appreciation for Prof. Lihua Xie for his unwavering commitment to advancing knowledge and look forward to the continued brilliance and innovation in the next chapters of his illustrious career.
Happy Birthday, Prof. Lihua Xie!

With the release of China's "dual carbon goals", individual investors' green concerns gradually cause strengthened pressure on listed companies. Using the data of investor interaction platforms of the Shanghai Stock Exchange and Shenzhen Stock Exchange, this paper constructs green concern indicators based on a sample of 2048 listed companies and empirically reveals that individual investors' green concerns can promote green innovation in terms of quantity and quality. This influence is more pronounced in non-heavily polluting industries and those with no pollution charges and fewer environmental protection subsidies, individual investors' green concerns play a supplement role in the environmental regulation policies. Improving the construction of the green financial system and guiding individual investors' green investment concept can promote listed companies' green innovation behaviors.

High-quality development focuses on total factor productivity. As a pioneer and demonstration zone of China's economic development, it is of great practical significance for national high-tech zones to effectively play a driving role in the region. Therefore, based on the data of Chinese prefecture-level cities from 2003--2018, the green total factor productivity was calculated as a measure of high-quality development. To alleviate the bias of the traditional DID model due to the neglect of policy spatial spillover, we constructed a spatial DID model to analyze the effect of the national high-tech zones on the green total factor productivity of local and neighboring cities. The results showed that the national high-tech zones increased the green total factor productivity. This policy had a clear promotion effect on cities without a national high-tech zone, and the effect was related to the original development level of the city and the spatial distance between cities; however, the cities with national high-tech zones has not shown a "strong team" or a "race to the top" spatial connection. In addition, the impact of national high-tech zones on regional green total factor productivity worked mainly through improving the regional innovation and entrepreneurship environment. Based on this, we suggest that the high-tech development highlands should to be created with national high-tech zones as the nodes, and the diversified regional cooperation system of national high-tech zones should to be built by breaking down the administrative boundaries.

In the context of the "double carbon" target, promoting the green and low-carbon transformation of economic and social development is a major systemic project. Developing renewable energy and improving energy efficiency will help to build a more efficient green energy system. Analyzing the effect of energy transformation has important reference value for formulating a reasonable carbon emission policy and achieving medium and long-term emission reduction targets. This study takes this as a starting point. Firstly, static panel and dynamic panel system generalized method of moments (GMM) are used to estimate the impact of energy transformation, renewable energy efficiency and non-renewable energy efficiency on major macroeconomic variables. Secondly, the intermediate production sector is subdivided into renewable energy production sector and non-renewable energy production sector. The dynamic stochastic general equilibrium (DSGE) model is constructed to analyze the short-term impact of energy transformation impact, renewable energy efficiency impact and non-renewable energy efficiency impact on major macroeconomic variables. The analysis shows that: 1) energy transformation promotes the transfer of resources between sectors, the output of renewable energy production sector will increase, while the output of non-renewable energy production sector and carbon emissions will decrease; 2) The improvement of two kinds of energy efficiency will produce economic expansion effect, but it will also produce energy rebound effect and increase carbon emissions; 3) At the end of the simulation period, the implementation of the carbon emission intensity policy will promote the growth effect of three shocks on output, but will also hinder the emission reduction effect and aggravate the rebound effect in the process of energy transformation. The implementation of the carbon tax policy will inhibit the rebound effect of two types of energy efficiency shocks on carbon emissions. In the process of energy transformation, we should rely on a reasonable carbon emission policy and formulate medium and long-term emission reduction targets. This study has important reference value for China to analyze the effect of energy transformation.

Climate damage and adaptation are key modules in the integrated assessment modelling on climate-economy system, both of which will directly affect the stringency of climate policy. This paper explores the recent academic advancement in these two aspects. As for climate damage, an emerging strand of literature not only focuses on the overall impact of rising temperature on economic production, but also sheds more spotlights on how rising temperature can cause economic losses through shocks to capital stock, labor supply, technical change and natural capital. Although recent studies illuminate directions for improving damage function under varying degrees of temperature rise, enormous disputes still hang over the concrete settings of the damage function. Existing climate-adaptation models commonly resort to either adaptive investment or market mechanisms. However, additional examinations should be warranted of related constraints on adaptation to avoid overestimating the effects of climate adaptation, as well as the regional heterogeneity in adaptation strategies. Future research should take into account social damages caused by climate change, other climate risk characteristics in addition to rising temperature, and how they channel corresponding effects. Furthermore, more empirical studies are needed to help refine the constructions and calibrations of the damage function, and probability tools are called for to investigate the uncertainties of climate damage and adaptation, in a shared effort to provide references for guiding climate policy practices.

In electronic structure calculations, Kohn-Sham equations rank among the most widely adopted mathematical models. However, due to the deficiency of available approximations for exchange-correlation energy, Kohn-Sham equations cannot well describe strongly correlated electrons systems at present. In recent decades, some researchers have studied the optimization models of strictly-correlated-electrons energy, starting from the strong-interaction limit of density functional theory. These models are hopeful to amend the very deficiency of Kohn-Sham equations and therefore broaden the applicability of the density functional theory. Since the curse of dimensionality resides in these models, some low-dimensional reformulations have been proposed. In this paper, we introduce the optimization models of strictly-correlated-electrons energy, highlight the research focus, and describe some lowdimensional reformulations. We also present the numerical approaches for these reformulations and shed light on the directions of future research.

Classical Mechanics Principle of Control Engineering (System) depends on that 3 state variables including position, velocity and acceleration can be accurately constructed and estimated for the controlled system output, and the 3 state variables are used to make negative feedback control functions for the control system based on Newton's Laws of Motion. In the reference paper (Kai P A and Shen Z L, 2022), an OUAM observer (Observer based on Uniform Acceleration Movement) is constructed by Kalman filter and an MFCNLM system (Model-Free Control based on Newton's Laws of Motion) is designed based on Newton's Laws of Motion. The desired transient process output of the closed loop system is designed with the desired transient process time T. All parameters in the control system are only calculated based on the desired transient process time T of system output without controlled plan model. From the reference paper (Kai P A and Shen Z L, 2022), the theory and application of Newton's Laws of Motion in the MFCNLM system and PIDCC system (PID Control with Compensator) are emphatically analyzed in the paper. The unbiased estimation of OUAM observer and the effectiveness of MFCNLM system are demonstrated. The unity of classical mechanics principle are explained in these control systems. A simple and effective method is designed for time-varying system with time-delay (dead-time) loop in the MFCNLM system and PIDCC system, 2 simulation examples in MFCNLM and PIDCC systems demonstrated fine control quality and robust performance of the design method in the paper.

Barrier option is a popular over-the-counter derivative in the Chinese market. Due to the discontinuity of its returns, financial institutions are mainly faced with the problem of delta value fluctuations in the process of dynamic hedging, resulting in higher hedging risks. We propose an efficient and stable willow tree method for barrier option pricing and greeks calculation for dynamic hedging of barrier options assuming the underlying asset price follows Merton's jump-diffusion model, which can also be extended to other stochastic processes. Compared with the existing methods, the willow tree method is more stable in calculating the delta, and the hedging cost is lower. An empirical analysis of the hedging effect of barrier options on the Shanghai Stock Exchange 50 Index is conducted from January 1, 2010 to September 30, 2021, and the model parameters are calibrated from the market data. The numerical results show that the willow tree method reduces hedging costs and hedging risks, and it can provide a new approach for domestic financial institutions to hedge barrier options and related structured products.

In light of recent events such as "sweatshops", "environmental pollution", and "child slave labor", it has become imperative for enterprises to shift their focus from solely pursuing short-term economic gains to fulfilling social responsibilities in order to achieve sustainable and high-quality development. Focusing on how to coordinate the participation of brand firms and suppliers in fulfilling the social responsibilities of the upstream supply chain and taking account of the prosociality and reference effects of consumers, we use differential game method to build the decision-making model of decentralized decision-making, centralized decision-making, and unilateral cost-sharing contract between brand firms and suppliers. Furthermore, we design the bilateral cost- and revenue-sharing contract to achieve perfect coordination of the supply chain. The results suggest that: 1) An increase in the proportion of prosocial consumers will prompt brand firms and suppliers to improve their efforts of fulfilling social responsibility. However, excessive reliance on brand reputation to determine the level of social responsibility performance of the upstream supply chain may reduce the willingness of supply chain members to fulfill their social responsibilities. 2) The unilateral cost-sharing contract encourages suppliers to improve the efforts of fulfilling social responsibility, but do not affect the brand firm's efforts of fulfilling social responsibility. The contract stimulates the improvement of brand goodwill and consumer reference prices, thus promote the increase of profits for brand firms and suppliers. Moreover, when the reference effect for the level of social responsibility fulfillment of the upstream supply chain has a significant impact on the formation of consumers' reference price, the reference price under this contract will instead be higher than the centralized decision-making model. However, the contract is difficult to promote brand firms and suppliers to jointly improve social responsibility performance, and cannot maximize the total profit of the supply chain. 3) When the revenue sharing ratio of brand firms is at a moderate level, it can promote both brand firms and suppliers to participate in a bilateral cost- and revenue-sharing contract, and coordinate the supply chain perfectly.

Common prosperity is the essential requirement of socialism and an important feature of Chinese modernization. This paper identifies the effect and mechanisms of digital economy on "Common Prosperity" from the perspective of income inequality by using the data of China household finance survey (CHFS). The empirical results show: 1) Digital economy can significantly reduce income inequality, which is still valid after we consider the endogeneity and perform corresponding robustness test. 2) The mechanism analysis shows that digital economy can effectively reduce household income inequality by mitigating the liquidity constraints, enhancing entrepreneurial activity and expanding household social network. 3) The heterogeneity analysis shows that the alleviating effect of digital economy on income inequality is more significant in the central and western provinces, rural areas and regions with lower level of digital economy, particularly for household with lower education, lower financial literacy, and larger digital divide, which signifies the inclusiveness of digital economy. Further discussion shows that all sub-indexes of digital economy can significantly narrow the household income gap, among which the digital dividend formed by digital effciency improvement index has a greater gain effect on it. Hence, this paper provides theoretical support and empirical evidence for promoting the development of China's digital economy and "Common Prosperity".

The real growth rate of gross domestic product (GDP) is an important indicator to measure the state of the economy. However, as it is released quarterly with a time lag, it fails to meet the timely economic analysis demand. In this paper, the mixed frequency dynamic factor model (MF-DFM) is used to nowcast quarterly GDP year-on-year growth rate based on timely large-scale monthly economic data, which improves the timeliness of economic analysis. In order to enhance the efficiency of utilizing a large number of available candidate economic variables and avoid the subjectivity of indicator selection in the factor model, this paper proposes a indicator selection method for MF-DFM with large-scale data, which uses the mean square prediction error of the binary dynamic single factor model as the basis for indicator selection. This method is applicable to selecting effective indicators amongst data with quarterly and monthly frequencies, missing values and jagged edges. The empirical analysis results indicate that compared with the traditional time series prediction models and the commonly used mixed frequency models, the MF-DFM based on screening variables by the binary model has a higher accuracy in predicting quarterly GDP growth rate, both for the stability period before COVID-19 and the recovery period after COVID-19. Moreover, the prediction for monthly GDP growth rate provided by this method has a high synchronization with the macroeconomic consistency index, which is conducive to improving the timeliness of economic analysis. This paper provides a new approach for real-time economic monitoring, prediction, and early warning based on indicator selection with the large-scale data.

Currently, the application of alternative data provides a new perspective for scholars and practitioners in the field of financial investment. This paper builds an analysis platform based on the FarmPredict (factor-augmented regularized model for prediction) framework and deep neural network model, realizing the task of learning trading signals from alternative data such as financial short videos and financial news thereby constructing trading strategies for the China share market. Firstly, match the captured financial news with their corresponding stock code and decompose it into text data and image data. Secondly, the text data is input into the FarmPredict learning framework. We construct and screen the text bag of words by which the phrases are decomposed into common factors and specific factors, and then calculate the score of the news text by the factor regression; We then input the image data into the image recognition deep neural network Google Inception v3 model framework built by the transfer learning technique, thereby outputting the probability that the image represents positive/negative emotions and the image sentiment index and image score. For the captured financial short video, it contains two steps. The first step is to strip the audio data and convert it to audio text data, and use the trained FarmPredict framework to calculate the text score of the short videos; the second step is to extract the key frames of the video, and use the trained image model to calculate the video image score; the text score is summed up with the image score to get the short video data score. Finally, the financial short video score, the text score and the image score of the news report are summed to obtain the stock investment signal, which is used as the basis for constructing the China share stock portfolio and formulating an appropriate investment strategy. Finally, the financial short video score, the text score and the image score of the news report are summed to obtain the stock investment signal, which is used as the basis for constructing the China share stock portfolio and formulating an appropriate investment strategy. The research results show that financial videos and financial news data contain information related to stock prices, which can effectively predict market changes and bring excess returns to investors. The empirical study confirms the importance of alternative data in the Chinese market. By comprehensively analyzing alternative data, this paper provides investors with a comprehensive and effective trading signal extraction method, which can help optimize investment strategies and achieve higher real returns.

Green merger and acquisition (M&A) is an important mechanism to achieve carbon peaking and carbon neutrality goal, but its motivation and environmental effects remain unclear. This paper uses the quasi-natural experiment of the segmented implementation of the ambient air quality standard as the exogenous impact to test the impact of environmental regulation on green M&A decisions of listed?companies. It is found that the implementation of ambient air quality standards can significantly improve the probability of green M&A of heavily polluting firms, and cause a significant improvement on firms' green innovation achievements and environmental performance. Further research shows that the green M&As are not concentrated in target areas with relatively lax environmental regulation, which eliminates the alternative explanation of pollution transfer through green M&A. Meanwhile, the green M&As promoted by ambient air quality standard are characterized by low proportion of related M&A, high proportion of major M&A and low proportion of performance commitments, which goes some way towards indicating that green M&As are not a "greenwashing". Finally, the green M&As promoted by ambient air quality standard mainly occur in non-state-owned firms and firms with high external attention. This paper makes an in-depth analysis of green M&A behaviors, which has enlightening significance for understanding the specific coping strategies of firms under the pressure of environmental governance policy, and also provides a useful reference for deepening the reform of environmental governance system.